arXiv:2608. 13510v1 Announce Type: cross Abstract: Machine learning procedures are commonly evaluated in terms of predictive accuracy and computational efficiency.
By Nestor R. Barraza, Gabriel Pena
arXiv:2602. 21889v2 Announce Type: replace-cross Abstract: Predictions from ML models support human decision making in several fields, including high-stakes ones such as healthcare and the judiciary.
By Otto Nyberg, Fausto Carcassi, Davide Tugnoli, Giovanni Cin\`a
arXiv:2407. 12288v5 Announce Type: replace-cross Abstract: The progress of machine learning over the past decade is undeniable.
By Hong Jun Jeon, Benjamin Van Roy
arXiv:2606. 19476v1 Announce Type: cross Abstract: Effective machine learning depends not only on how we model data, but also on what data we choose to collect.
By Eric Elmoznino, Sangnie Bhardwaj, Johannes von Oswald, Rajai Nasser, Blaise Ag\"uera y Arcas, Jo\~ao Sacramento, Rif A. Saurous, Guillaume Lajoie
Effective machine learning depends not only on how we model data, but also on what data we choose to collect. While large sequence models have revolutionized data modeling, the problem of automated data selection, or "intrinsic curiosity", remains a significant challenge.
arXiv:2606. 27711v1 Announce Type: cross Abstract: We introduce a neural network-based framework for learning time series estimators through a process we term decision-theoretic pretraining.
By Pablo Montero-Manso, Marcel Scharth